Zusammenfassung

Sleep spindles are frequently studied for their relationship with state
and trait cognitive variables, and they are thought to play an important
role in sleep-related memory consolidation. Due to their frequent
occurrence in NREM sleep, the detection of sleep spindles is only
feasible using automatic algorithms, of which a large number is
available. We compared subject averages of the spindle parameters
computed by a fixed frequency (FixF) (11-13 Hz for slow spindles, 13-15
Hz for fast spindles) automatic detection algorithm and the individual
adjustment method (IAM), which uses individual frequency bands for sleep
spindle detection. Fast spindle duration and amplitude are strongly
correlated in the two algorithms, but there is little overlap in fast
spindle density and slow spindle parameters in general. The agreement
between fixed and manually determined sleep spindle frequencies is
limited, especially in case of slow spindles. This is the most likely
reason for the poor agreement between the two detection methods in case
of slow spindle parameters. Our results suggest that while various
algorithms may reliably detect fast spindles, a more sophisticated
algorithm primed to individual spindle frequencies is necessary for the
detection of slow spindles as well as individual variations in the
number of spindles in general.